A knowledge identification framework for the engineering of ontologies in system composition processes

Recent research has been focused on the creation of intelligent compositional systems that utilize ontologies as a knowledge base to facilitate the composition of new systems/workflows. Within this ontology-driven compositional systems field, experts have created knowledge representation models to satisfy requirements of their own domain rather than considering a general perspective. This paper proposes a knowledge identification framework to facilitate collaborative decision-making during knowledge requirement gathering to assist in the capture, merging, and mapping within an ontology engineering methodology. Five categories of knowledge (and a mapping of their relationships) are recognized as knowledge elements that should at least be considered in any representation model. A differentiation of syntactic and semantic knowledge, and a depiction of external influences on the composition process is also included. The paper concludes that while the presented framework does not guarantee an optimal ontological model, it does assist with the knowledge identification process for single or multiple stakeholders in ontology engineering for compositional systems.